System and Method for Patient Care Improvement

ABSTRACT

Described is a system comprising an input module receiving a first data set indicative of at least one patient condition for each of a plurality of patients obtained during a predetermined time period, a comparison module comparing each of the at least one patient condition to at least one filter criteria, a filter module selecting a patient to include in a second data set if the at least one patient condition of the patient satisfies the at least one filter criteria, a report module generating a report based on the second data set, wherein the report includes at least one patient identifier for each patient in the second data set and at least one descriptor of the at least one patient condition for each patient in the second data set, and a classification module storing at least one classification value for the at least one patient condition.

TECHNICAL FIELD

The invention relates to systems and methods for patient careimprovement.

BACKGROUND OF THE INVENTION

Uncontrolled blood sugar continues to impact organizations (e.g.,hospitals) both clinically and financially. The organizations havevarious ways of measuring and analyzing glycemic management performance(sometimes referred to as “glucometrics”). There are four major problemsassociated with conventional analysis of glycemic managementperformance. First, glucose measurements taken when a patient isadmitted are typically discarded, because the organization may feel thatit was not responsible for the patient pre-admittance. Thus, theorganization may feel initial glucose measurement may unfairly bias theanalysis of its ability to control its patients' blood sugars. Second,the organization may not be using insulin properly to control thepatient's blood sugar. For example, incorrect doses or insulin types maybe administered, insulin may be administered at incorrect times or notat all, and/or future doses may not be modified in view of interveningblood glucose results. Third, the organization typically utilizes aretrospective analysis after collecting at least one month of glucosemeasurement and glycemic management performance data. Fourth, theorganization, after performing its retrospective analysis, may take atleast one month to implement a change to its glycemic managementprotocols. By waiting at least one month to collect data and at leastone further month to implement a change to the glycemic managementprotocols, the organization is responding to uncontrolled blood sugarissues with at least a two month delay.

There remains a need for an improved system and method for patient careimprovement.

SUMMARY OF THE INVENTION

In an exemplary embodiment, a system for patient care improvementcomprises an input module receiving a first data set indicative of atleast one patient condition for each of a plurality of patients obtainedduring a predetermined time period, a comparison module comparing eachof the at least one patient condition to at least one filter criteria, afilter module selecting a patient to include in a second data set if theat least one patient condition of the patient satisfies the at least onefilter criteria, a report module generating a report based on the seconddata set, wherein the report includes at least one patient identifierfor each patient in the second data set and at least one descriptor ofthe at least one patient condition for each patient in the second dataset, and a classification module storing at least one classificationvalue for the at least one patient condition.

In an exemplary embodiment, the comparison module compares each of theplurality of patients to a predetermined patient type. The filter modulemay not include a patient to include in the second data set if thepatient satisfies the predetermined patient type. The predeterminedpatient type may be based on patient age and time elapsed since thepatient was admitted.

In an exemplary embodiment, the input module includes at least one of agraphical user interface and a database. The graphical user interfaceincludes a website and/or a software program.

In an exemplary embodiment, the at least one patient condition includesat least one of a glucose measurement, an admission diagnosis, and anexistence of an insulin pump.

In an exemplary embodiment, the predetermined time period is twenty-fourhours. In another exemplary embodiment, the predetermined time periodbegins at least twenty-four hours after the patient is admitted.

In an exemplary embodiment, the at least one filter criteria includes atleast one of a glucose measurement below approximately 70 mg/dL, aglucose measurement above approximately 300 mg/dL, a diagnosis ofdiabetic ketoacidosis, a diagnosis of a hypersomolor hyperglycaemicstate, a diagnosis of gestational diabetes mellitus, a hypoglycaemicstate, an existence of an insulin pump, a diagnosis of Type 1 diabetes,a diagnosis of Type 2 diabetes, an existence ofintravenously-administered insulin.

In an exemplary embodiment, the at least one patient identifier is not aHealth Insurance Portability and Accountability Act (HIPAA) ProtectedHealth Information.

In an exemplary embodiment, the at least one descriptor corresponds tothe at least one filter criteria.

In an exemplary embodiment, the system further comprises a storagemodule storing the report in a database. In an exemplary embodiment, thesystem further comprises an access module limiting access to the reportbased on predetermined authorization criteria. In an exemplaryembodiment, the predetermined authorization criteria include at leastone of a username, a password, a pin number, and a biometric.

In an exemplary embodiment, a healthcare provider identifier isassociated with each of the at least one patient identifier in thereport. In an exemplary embodiment, the system further comprises analert module transmitting an alert message to an address associated withthe healthcare provider identifier. The alert message may include atleast one of the at least one patient identifier and the at least onedescriptor.

In an exemplary embodiment, the system further comprises arecommendation module generating a treatment recommendation based on theat least one descriptor for each patient in the second data set. In anexemplary embodiment, the system further comprises a progress modulestoring the treatment recommendation and a progress value indicative ofperformance of the treatment recommendation.

Further scope of applicability of the present invention will becomeapparent from the detailed description given hereinafter. However, itshould be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only, and thus, are not limitiveof the present invention, and wherein:

FIG. 1 shows an exemplary embodiment of a system for patient careimprovement according to the present invention;

FIG. 2 shows an exemplary embodiment of a central computer according tothe present invention;

FIG. 3 shows an exemplary embodiment of an alert message according tothe present invention;

FIG. 4 shows another exemplary embodiment of an alert message accordingto the present invention;

FIG. 5 shows an exemplary embodiment of a patient conditionclassification form according to the present invention; and

FIG. 6 shows an exemplary embodiment of a method for patient careimprovement according to the present invention.

Corresponding parts are marked with the same reference symbols in allfigures.

DETAILED DESCRIPTION

In an exemplary embodiment, the present invention relates to systems andmethods for patient care improvement. Although the exemplary embodimentis described with reference to systems and methods for improvingglycemic management in a hospital, those of skill in the art willunderstand that the systems and methods may be utilized for managementof other patient parameters such as, for example, pain management,cardio-vascular, etc., and that the systems and methods of the presentinvention may be utilized in other clinical (e.g., out-patient clinics,elder care facilities, veterinarians, etc.) and non-clinical settings(e.g., homes, schools). Further, the systems and methods of the presentinvention may be utilized in human care, as well as animal care.

FIG. 1 shows an exemplary embodiment of a system 10 for patient careimprovement according to the present invention. The system 10 mayinclude a central computer 100 which is adapted to communicate with oneor more remote computers 200. The central computer 100 may includehardware (e.g., one or more servers, databases, storage media, networkcommunication device, etc.) or software (e.g., one or more softwareprograms), or any combination thereof. The central computer 100 maycommunicate with the remote computers 200 via wired or wirelesscommunications systems. The remote computers 200 may include PCs,laptops, PDAs, tablets, cellphones, scanners, copiers, printers, kiosks,etc. In an exemplary embodiment, data may be communicatedbidirectionally between the central computer 100 and the remotecomputers 200. In another exemplary embodiment, the remote computers 200may be used to gather data for transmission to the central computer 100.

In an exemplary embodiment, the system 10 may be implemented in ahospital. For example, the central computer 100 may be a server operatedby the hospital, and the remote computers 200 may be associated withindividuals (e.g., PDAs of hospital personnel), hospital rooms, hospitalareas (e.g., ER, ICU), etc. In an exemplary embodiment, a user of aremote computer 200 may be required to provide authorization information(e.g., username, password, PIN, biometric) to use the remote computer200 and/or have access to information stored on the central computer100. In another exemplary embodiment, the hospital personnel may utilizethe remote computers 200 to interface with the central computer 100which may be located outside of the hospital.

FIG. 2 shows an exemplary embodiment of the central computer 100according to the present invention. The central computer 100 includes aninput module 105 adapted to receive a first data set indicative of atleast one patient condition for each of a plurality of patients. The atleast one patient condition includes at least one of a glucosemeasurement, an admission diagnosis and an existence of an insulin pump.In an exemplary embodiment, a glucose measurement may be taken by afinger-stick. The admission diagnosis may be determined when a patientis admitted to the hospital. The existence of an insulin pump used bythe patient may be determined at a time of admission of the patient orduring a time in which the patient is admitted to the hospital.

In an exemplary embodiment, the input module 105 may provide a userinterface, e.g., a website or a software program, which is accessible bythe remote computers 200.

When the patient condition is identified, data identifying the patientcondition may be entered into the user interface and uploaded to thecentral computer 100. For example, a glucose measurement, an admissiondiagnosis, and/or the existence of an insulin pump for a patient may beentered into an electronic form field and then uploaded to the centralcomputer 100. In another exemplary embodiment, the data identifying thepatient condition may be stored in a storage device on the remotecomputer 200 and uploaded to the central computer 100 at a predeterminedtime. For example, the remote computers 200 in the hospital may beprogrammed to upload all of the data collected regarding the patientconditions to the central computer after normal business hours.

In an exemplary embodiment, the data identifying the patientcondition(s) for a plurality of patients in the hospital is aggregatedby the input module 105 by predetermined time period. For example, theinput module 105 may create the first data set by including all datarelated to patient conditions collected over a 24-hour period. Thus, inthis exemplary embodiment, the first data set represents a one-daysnapshot of all patient conditions for the patients (or subset thereof)in the hospital.

In an exemplary embodiment, the central computer 100 includes acomparison module 110 that compares the patient condition of eachpatient in the first data set to at least one filter criteria. Forexample, the patient condition associated with the first patient entryin the first data set may be compared sequentially or in parallel to oneor more of the filter criteria. In an exemplary embodiment, the filtercriteria is selected based on the intended patient care improvement. Forexample, in glycemic control embodiment, the filter criteria mayinclude, but is not limited to, a glucose measurement belowapproximately 70 mg/dL, a glucose measurement above approximately 300mg/dL, a diagnosis of diabetic ketoacidosis, a diagnosis of ahypersomolor hyperglycaemic state, a diagnosis of gestational diabetesmellitus, a hypoglycaemic state, an existence of an insulin pump, adiagnosis of Type 1 diabetes, a diagnosis of Type 2 diabetes, anexistence of intravenously-administered insulin. Those of skill in theart will understand that the filter criteria may be customized for anymetric which is useful for patient care improvement. For example, in apain management embodiment, the filter criteria may include, but is notlimited to, an amount of pain medication taken, a type of painmedication taken, whether the pain medicament was generic, a pain ratingby the patient before and/or after taking the pain medication, etc.

In another exemplary embodiment, the comparison module 110 may alsocompare each patient identified in the first data set to a predeterminedpatient type. For example, it may be desirable to exclude patientcondition data obtained from patients who do not conform to the patientpopulation being analyzed. In one exemplary embodiment, the patient typemay be an age threshold (e.g., to exclude children or adults). Inanother exemplary embodiment, the patient type may he indicative of aduration for which the patient has been admitted (e.g., to excludepatients that have been admitted for less than 24 hours).

In an exemplary embodiment, the central computer 100 includes a filtermodule 115 which selected a patient identified in the first data set toinclude in a second data set, which is a subset of the first data set,if the patient condition(s) associated with the patient satisfies atleast one of the filter criteria. In the exemplary embodiment, thefilter module 115 uses a positive-match inclusion process; however,those of skill in the art will understand that a positive-matchexclusion process, a negative-match inclusion process or anegative-match exclusion process may be utilized. For example, if thepatient condition is a glucose measurement which is under 70 mg/dL andthe filter criteria includes a glucose measurement below approximately70 mg/dL, the patient may be selected by the filter module 115 forinclusion in the second data set.

Those of skill in the art will understand that the comparison module 110and the filter module 115 may operate continuously over thepredetermined time period to generate the second data set, or mayoperate once after all of the patient condition data has been collectedover the predetermined time period.

In an exemplary embodiment, the central computer 100 includes a reportmodule 120 that generates a report based on the second data set. In anexemplary embodiment, the report may include at least one patientidentifier for each patient in the second data set and at least onedescriptor of the at least one patient condition for each patient in thesecond data set. The patient identifier is at least one value that isnota Health Insurance Portability and Accountability (HIPAA) ProtectedHealth Information (PHI). The descriptor may be a text description ofthe filter criteria, and the descriptor selected for a given patient maycorrespond to the filter criteria that was satisfied by the patientcondition(s) of the given patient. For example, the descriptor may be“FSG<70 mg/dL” to indicate that the patient condition was a glucosemeasurement that was less than 70 mg/dL, which satisfied the glucosemeasurement less than 70 mg/dL filter criteria.

The report may be stored by a storage module 125 that saves the reporton a storage device within the central computer 100 or a database incommunication with the central computer 100.

In an exemplary embodiment, access to the report may be limited. Forexample, an access module 130 on the central computer 100 may requireauthorization criteria to access (e.g., view, modify, etc.) the report.The authorization criteria may be entered into the central computer 100via the input module 105.

In an exemplary embodiment, a healthcare provider e.g., physician,nurse, out-patient caregiver, family member, etc.) may have anidentifier which is associated with each entry in the second data set.For example, a physician identifier (e.g., name, ID number, etc.) may beassociated with each patient in the second data set that the physicianis responsible for. Thus, the report module 120 can filter and/or sortor the report, or generate a sub-report, that identifies all of thepatients in the second data set which a given healthcare provider isresponsible for.

In an exemplary embodiment, the central computer 100 includes an alertmodule 135 for transmitting an alert message to the healthcare provider.For example, if a patient the healthcare provider is responsible for isidentified in the second data set, the alert module 135 may generate analert message identifying the patient using the patient identifier andincluding the descriptor to inform the healthcare provider about why thepatient was included in the second data set. The central computer 100may include an address (e.g., phone number, fax number, mailing address,email address, instant message address, pager number, etc.) which thealert message is sent to. In an exemplary embodiment, the full alertmessage is sent to the address of the healthcare provider. In anotherexemplary embodiment, the alert message may include a hyperlink to thefull alert message.

FIG. 3 shows an exemplary embodiment of an alert message 300 accordingto the present invention. The alert message 300 includes a plurality ofdescriptor fields 305, and the descriptor(s) that correspond to thepatient may be marked.

FIG. 4 shows another exemplary embodiment of an alert message 400according to the present invention. The alert message 400 includes apatient identifier 405 and a descriptor 410.

Those of skill in the art will understand that the alert message 400 maybe generated by a template on the central computer 100 which ispopulated with information from the second data set.

In an exemplary embodiment, the central computer 100 includes arecommendation module 140 that generates a treatment recommendationbased on the descriptor(s) for each patient identified in the seconddata set. The treatment recommendation may be included in the reportand/or the alert message. For example, as shown in FIG. 3, a treatmentrecommendation 315 may be included in the alert message 300. As shown inFIG. 4, a treatment recommendation 415 may be included in the alertmessage 400.

In an exemplary embodiment, the treatment recommendation can bedetermined based on available resources. For example, credentials ofhealthcare providers, staffing time, patient type, lab value,number/history of e.g., glycyemic events, administrative resources,cause/classification of patient event and day of the week may beconsidered when generating the treatment recommendation. For example,the recommendation module 140 may analyse the descriptor(s) for a givenpatient and identify (i) one or more healthcare providers who haveexpertise in addressing similar patients, (ii) the availability of suchhealthcare providers, and (iii) the medical history (short-term orlong-term) of this patient. The results of this analysis may be used togenerate the treatment recommendation.

In an exemplary embodiment, the central computer 100 includes a progressmodule 145 storing the treatment recommendations for each patientidentified in the second data set. The progress module 145 may furtherinclude a progress value indicative of performance of the treatmentrecommendation for a given patient. For example, if a healthcareprovider performs a service (e.g., insulin regimen review/modification,consult with other healthcare providers, etc.) identified by and/orassociated with the treatment recommendation, the healthcare providermay input treatment data into a remote computer 200 for transmission tothe central computer 100. The progress module 145 may compare thetreatment data to the treatment recommendation and generate a progressvalue indicating whether the treatment recommendation was followed. Inthis manner, the healthcare provider (or any other administrative ormonitoring entity) may track the accuracy and efficacy of the treatmentrecommendations. The report module 120 may be configured to generate areport of the treatment recommendations and progress values for a givenpatient to, for example, track the impact of the treatmentrecommendations on a positive or negative outcome on the health and careof the given patient.

In an exemplary embodiment, the central computer 100 includes aclassification module 150 for storing at least one classification valuefor the at least one patient condition. In an exemplary embodiment, theclassification module 150 may receive input through manually entereddata indicative of possible factors which contributed to an onset of thepatient condition. For example, FIG. 5 shows an exemplary embodiment ofa patient condition classification form 500 having a plurality offields. In an exemplary embodiment, the form 500 may be provided by anindustry or patient-care group. For example, the form 500 could be aGlycemic Control Performance Improvement Work Sheet provided by theAmerican Association of Clinical Endocrinologists. In another exemplaryembodiment, the form 500 may be specially generated for the application.The form 500 may be electronic and implemented on a remote computer 200,providing data to the central computer 100 via the input module 105, orthe form 500 may be a hardcopy which is scanned and converted intoelectronic form for into to the input module 105. For each instance inwhich a patient condition 510 meets one of the filter criteria, ahealthcare provider selects one or more possible factors 505 whichcontributed to an onset of the patient condition 510. The classificationmodule 150 uses the input provided on the patient conditionclassification form 500 to generate a classification value for the atleast one patient condition. In an exemplary embodiment, therecommendation module 145 may utilize the classification value togenerate the treatment recommendation. For example, if theclassification value corresponds to a change in caloric intake, therecommendation module 145 may generate a treatment recommendation whichsuggests raising or lowering the caloric intake for the patient.

In another exemplary embodiment, the classification module 150 maygenerate the classification value automatically by, for example,analyzing data associated with patient activity, patient eatingpatterns, patient treatment, patient medication, administrativeactivity, etc., which may be stored on or accessible by the centralcomputer 100. For example, if a patient condition meets one of thefilter criteria, the classification module 150 may analyze the patient'scaloric intake for a predetermined time period prior to the onset of thepatient condition. If the patient's caloric intake had decreased,relative to previous days, in the twenty-four hours prior to theidentification of the patient condition, the classification module 150may generate a classification value for the patient condition whichcorresponds to a “change in caloric intake” factor 505. Therecommendation module 140 may utilize the classification value togenerate a treatment recommendation suggesting that the patient increaseher caloric intake (and optionally, include a caloric intake fromprevious time periods in which the patient condition was notidentified). The alert message may include the classification value sothat the healthcare provider understands the basis for the treatmentrecommendation.

While the exemplary embodiment of the central computer 100 describes thevarious modules as being stored on the central computer 100, in otherexemplary embodiments one or more of the modules may be distributedacross one or more other computers, and the modules may include orutilize one or more microprocessors and/or storage devices.

FIG. 6 shows an exemplary embodiment of a method 600 for patient careimprovement according to the present invention. The followingdescription of the exemplary method 600 incorporates the description ofthe structures and algorithms described above.

In step 605, the first data set indicative of at least one patientcondition for each of a plurality of patients obtained during apredetermined time period is received. The first data set may becompiled as data regarding patient conditions is received by the centralcomputer 100, or the first data set may be compiled at a predeterminedtime (e.g., after the predetermined time period has ended).

In step 610, each patient condition in the first data set is compared toat least one filter criteria. Those of skill in the art will understandthat such comparison may be string-based comparison using thealphanumeric text of the patient condition and the filter criteria, orthe patient condition and the filter criteria may be represented asnumbers or short expressions for the comparison.

In steps 615 and 620, the first data set is filtered to generate asecond data set by including only patients from the first data set whosepatient condition satisfies at least one of the filter criteria. If agiven patient condition does not satisfy at least one of the filtercriteria, a the patient condition for another patient in the first dataset is analyzed until the patient conditions for all of the patients inthe first data set have been analyzed. In another exemplary embodiment,the first data set if filtered to generate a second data set by removingpatients from the first data set who patient conditions do not satisfyat least one of the filter criteria.

In step 625, a report is generated based on the second data set. In anexemplary embodiment, the report includes at least one patientidentifier for each patient in the second data set and at least onedescriptor of the at least one patient condition for each patient in thesecond data set. In step 640, prior to the report being generated, aclassification value may be generated and associated with the patientcondition for each patient in the second data set. In step 645, prior tothe report being generated, a treatment recommendation may be generatedand associated with the patient condition for each patient in the seconddata set. Thus, the report generated in step 625 may include a recordfor each patient identified in the second data set, and the record mayinclude the patient identifier, the descriptor, the classification value(and/or description) and/or the treatment recommendation.

In steps 630 and 635, an alert message may be generated and transmittedto a healthcare provider associated with a given patient identified inthe second data set. The alert message may include any or all of theinformation in the report.

Those of skill in the art will understand that modifications (additionsand/or removals) of various components of the apparatuses, methodsand/or systems and embodiments described herein may be made withoutdeparting from the full scope and spirit of the present invention, whichencompass such modifications and any and all equivalents thereof.

1. A system, comprising: an input module receiving a first data setindicative of at least one patient condition for each of a plurality ofpatients obtained during a predetermined time period; a comparisonmodule comparing each of the at least one patient condition to at leastone filter criteria; a filter module selecting a patient to include in asecond data set if the at least one patient condition of the patientsatisfies the at least one filter criteria; a report module generating areport based on the second data set, wherein the report includes atleast one patient identifier for each patient in the second data set andat least one descriptor of the at least one patient condition for eachpatient in the second data set; and a classification module storing atleast one classification value for the at least one patient condition,2. The system according to claim 1, wherein the comparison modulecompares each of the plurality of patients to a predetermined patienttype.
 3. The system according to claim 2, wherein the filter module doesnot include a patient to include in the second data set if the patientsatisfies the predetermined patient type.
 4. The system according toclaim 3, wherein the predetermined patient type is based on patient ageand time elapsed since the patient was admitted.
 5. The system accordingto claim 1, wherein the input module includes at least one of agraphical user interface and a database.
 6. The system according toclaim 5, wherein the graphical user interface includes a website.
 7. Thesystem according to claim 5, wherein the graphical user interfaceincludes a software program.
 8. The system according to claim 1, whereinthe at least one patient condition includes at least one of a glucosemeasurement, an admission diagnosis, and an existence of an insulinpump.
 9. The system according to claim 1, wherein the predetermined timeperiod is twenty-four hours.
 10. The system according to claim 1,wherein the predetermined time period begins at least twenty-four hoursafter the patient is admitted.
 11. The system according to claim 1,wherein the at least one filter criteria includes at least one of aglucose measurement below approximately 70 mg/dL, a glucose measurementabove approximately 300 mg/dL, a diagnosis of diabetic ketoacidosis, adiagnosis of a hypersomolor hyperglycaemic state, a diagnosis ofgestational diabetes mellitus, a hypoglycaemic state, an existence of aninsulin pump, a diagnosis of Type 1 diabetes, a diagnosis of Type 2diabetes, an existence of intravenously-administered insulin.
 12. Thesystem according to claim 1, wherein the at least one patient identifieris not a Health Insurance Portability and Accountability Act (HIPAA)Protected Health Information.
 13. The system according to claim 1,wherein the at least one descriptor corresponds to the at least onefilter criteria.
 14. The system according to claim 1, furthercomprising: a storage module storing the report in a database.
 15. Thesystem according to claim 14, further comprising: an access modulelimiting access to the report based on predetermined authorizationcriteria.
 16. The system according to claim 15, wherein thepredetermined authorization criteria include at least one of a username,a password, a pin number, and a biometric.
 17. The system according toclaim 1, wherein a healthcare provider identifier is associated witheach of the at least one patient identifier in the report.
 18. Thesystem according to claim 17, further comprising: an alert moduletransmitting an alert message to an address associated with thehealthcare provider identifier.
 19. The system according to claim 18,wherein the alert message includes at least one of the at least onepatient identifier and the at least one descriptor.
 20. The systemaccording to claim 1, further comprising: a recommendation modulegenerating a treatment recommendation based on the at least onedescriptor for each patient in the second data set.
 21. The systemaccording to claim 20, further comprising: a progress module storing thetreatment recommendation and a progress value indicative of performanceof the treatment recommendation.